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Increasing Rigor in Online Health Surveys Through the Reduction of Fraudulent Data.

Wen Zhi Ng1, Sundarimaa Erdembileg1,2, Jean C J Liu3

  • 1Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, 12 Science Drive 2, #10-01, Singapore, 117549, Singapore, 65 91878576.

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Summary

Ensuring data integrity in online health research surveys is crucial. This study synthesizes practical strategies to detect and remove fraudulent responses, enhancing the rigor of online data collection.

Keywords:
data integritydata qualitydata validationfraudfraudulent responsesmethodological rigoronline surveysrecruitment strategiessurvey fraudweb-based researchweb-based surveys

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Area of Science:

  • Health Research Methodology
  • Data Integrity in Digital Studies

Background:

  • Online surveys are vital for modern health research, offering cost-effective data collection and access to diverse populations.
  • However, challenges exist in ensuring data integrity due to sophisticated fraudulent responses (bots, repeat responders).

Purpose of the Study:

  • To provide a comprehensive synthesis of practical strategies for detecting and removing fraudulent data in online surveys.
  • To enhance the rigor of online health research through improved data collection and validation techniques.

Main Methods:

  • Integration of automated screening techniques (e.g., CAPTCHAs, honeypot questions) and attention checks (e.g., trap questions).
  • Implementation of robust recruitment procedures (e.g., concealed eligibility criteria, 2-stage screening) and appropriate incentive structures.
  • Examination of sampling methodologies (river sampling, online panels, crowdsourcing) and post-data collection analysis (metadata, response patterns).

Main Results:

  • Proposed dynamic protocols combining multiple strategies for dynamic fraud detection and data filtering.
  • Highlighted the need for continuous adaptation to evolving fraud tactics, including AI-driven methods.
  • Emphasized the importance of pre-data collection, during-collection, and post-collection strategies for robust fraud detection.

Conclusions:

  • Robust strategies to screen for fraudulent data are essential for upholding scientific integrity in online health research.
  • Further research is needed to develop and refine methods for combating increasingly sophisticated fraud tactics.
  • A multipronged, adaptive approach is recommended to effectively filter fraudulent data and ensure reliable research outcomes.